BioTransition is a comprehensive R package for detecting critical transitions in biological systems using Dynamic Network Biomarker (DNB) theory. Critical transitions—abrupt shifts between alternative stable states—are ubiquitous in complex biological processes, including disease onset, cellular differentiation, and developmental transitions. Identifying the pre-transition state (tipping point) before an irreversible shift occurs is crucial for early warning and therapeutic intervention.
This package implements seven complementary DNB methodologies, including tDNB (topological DNB), a novel algorithm that leverages network topology and scale-free properties for robust critical transition detection.
The DNB theory, proposed by Chen et al. (2012), provides a model-free approach to detect early warning signals of critical transitions. The core principle is that a dominant group of molecules (DNB module) exhibits three key characteristics near the tipping point:
- Increased variation: Standard deviation (SD) of DNB members increases dramatically
- Enhanced correlation: Pearson correlation coefficient (PCC) among DNB members strengthens
- Decreased correlation: PCC between DNB members and non-DNB molecules weakens
The composite index (CI) quantifies the transition signal:
| Method | Description | Network | Reference |
|---|---|---|---|
| cDNB | Conventional DNB based on correlation screening | — | Chen et al., 2012 |
| tDNB | Topological DNB using scale-free network topology | — | Liu Z. (this package) |
| LcDNB | Local DNB with protein-protein interaction constraints | PPI | — |
| LDNB | Landscape DNB for state transition quantification | PPI | Liu et al., 2019 |
| MDNB | Module-based DNB for modular network analysis | PPI | Li et al., 2022 |
| TSNMB | Time-series network module biomarker | PPI | Zhong et al., 2022 |
| TSLE | Time-series leading edge analysis | PPI | Liu et al., 2020 |
# Install from R-universe (recommended)
install.packages("BioTransition", repos="https://zaoqu-liu.r-universe.dev")
# Or install from GitHub# install.packages("devtools")devtools::install_github("SolvingLab/BioTransition")library(BioTransition)
# Prepare sample annotationsample_info<-data.frame(
sample_id= colnames(expression_matrix),
state=state_labels
)
# Run tDNB analysis (topological DNB)result<- tDNB(
expr=expression_matrix,
state=sample_info,
state.levels= c("Normal", "Pre-disease", "Disease")
)
# Examine resultsresult$DNB.score# Composite index across statesresult$DNB.genes# Identified DNB module genesresult$Candidate# Candidate modules per stateBioTransition supports multiple omics data types:
- Bulk RNA-seq: Population-level transcriptomics
- Single-cell RNA-seq: Cell-type specific analysis
- Spatial transcriptomics: Spatially resolved expression data
The package includes curated protein-protein interaction (PPI) networks:
ppi_h: Human PPI network from STRING databaseppi_m: Mouse PPI network from STRING database
Core routines are heavily optimised: correlation analysis and sample-specific
network construction run in C++ via Rcpp, while module scoring in cDNB/tDNB
uses a vectorised complement-identity formulation that is ~19× faster than a
naive per-module loop on large inputs.
Chen L, Liu R, Liu ZP, Li M, Aihara K. (2012). Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Scientific Reports, 2:342. doi: 10.1038/srep00342
Liu R, Chen P, Chen L. (2019). Single-sample landscape entropy reveals the imminent phase transition during disease progression. National Science Review, 7(7):1175-1185. doi: 10.1093/nsr/nwy162
Li M, Zeng T, Liu R, Chen L. (2022). Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis. The Innovation, 3(5):100364. doi: 10.1016/j.xinn.2022.100364
Zhong J, Han C, Wang Y, Chen P, Liu R. (2022). Identifying the critical state of complex diseases by the novel concept of sample-specific network module biomarker. Journal of Molecular Cell Biology, 14(6):mjac052. doi: 10.1093/jmcb/mjac052
Liu X, Chang X, Liu R, Yu X, Chen L, Aihara K. (2020). Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. Bioinformatics, 36(4):1068-1074. doi: 10.1093/bioinformatics/btz758
If you use BioTransition in your research, please cite:
Liu Z (2026). BioTransition: Dynamic Network Biomarker Analysis for Critical Transition Detection. R package version 2.1.0. https://github.com/SolvingLab/BioTransition
Zaoqu Liu, MD, PhD
Chinese Academy of Medical Sciences and Peking Union Medical College
Email: liuzaoqu@163.com
ORCID: 0000-0002-0452-742X
GPL (>= 3)